


'''simple docstring'''
import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint


def       _lowercase							(					UpperCamelCase__							:       Any      ):
 # encoder.embeddings are double copied in original FLAVA
 return sum(param.float().sum() if 'encoder.embeddings' not in key else 0 for key, param in state_dict.items()      )


def       _lowercase							(					UpperCamelCase__							:       str,					UpperCamelCase__							:       Tuple      ):
 __A	:		List[Any]	  =      {}

 for key, value in state_dict.items():
  if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
   continue

  __A	:		Optional[Any]	  =      key.replace('heads.cmd.mim_head.cls.predictions',					'mmm_image_head'      )
  __A	:		Dict	  =      key.replace('heads.cmd.mlm_head.cls.predictions',					'mmm_text_head'      )
  __A	:		str	  =      key.replace('heads.cmd.itm_head.cls',					'itm_head'      )
  __A	:		Optional[int]	  =      key.replace('heads.cmd.itm_head.pooler',					'itm_head.pooler'      )
  __A	:		List[Any]	  =      key.replace('heads.cmd.clip_head.logit_scale',					'flava.logit_scale'      )
  __A	:		int	  =      key.replace('heads.fairseq_mlm.cls.predictions',					'mlm_head'      )
  __A	:		List[Any]	  =      key.replace('heads.imagenet.mim_head.cls.predictions',					'mim_head'      )
  __A	:		Optional[Any]	  =      key.replace('mm_text_projection',					'flava.text_to_mm_projection'      )
  __A	:		Dict	  =      key.replace('mm_image_projection',					'flava.image_to_mm_projection'      )
  __A	:		str	  =      key.replace('image_encoder.module',					'flava.image_model'      )
  __A	:		Any	  =      key.replace('text_encoder.module',					'flava.text_model'      )
  __A	:		List[str]	  =      key.replace('mm_encoder.module.encoder.cls_token',					'flava.multimodal_model.cls_token'      )
  __A	:		Tuple	  =      key.replace('mm_encoder.module',					'flava.multimodal_model'      )
  __A	:		Optional[int]	  =      key.replace('text_projection',					'flava.text_projection'      )
  __A	:		Optional[Any]	  =      key.replace('image_projection',					'flava.image_projection'      )

  __A	:		List[str]	  =      value.float()

 for key, value in codebook_state_dict.items():
  __A	:		Any	  =      value

 return upgrade


@torch.no_grad()
def       _lowercase							(					UpperCamelCase__							:       List[str],					UpperCamelCase__							:       Optional[Any],					UpperCamelCase__							:       Dict,					UpperCamelCase__							:       int=None      ):
 if config_path is not None:
  __A	:		Optional[Any]	  =      FlavaConfig.from_pretrained(UpperCamelCase__      )
 else:
  __A	:		List[Any]	  =      FlavaConfig()

 __A	:		Any	  =      FlavaForPreTraining(UpperCamelCase__      ).eval()

 __A	:		Dict	  =      convert_dalle_checkpoint(UpperCamelCase__,					UpperCamelCase__,					save_checkpoint=UpperCamelCase__      )

 if os.path.exists(UpperCamelCase__      ):
  __A	:		Dict	  =      torch.load(UpperCamelCase__,					map_location='cpu'      )
 else:
  __A	:		Optional[Any]	  =      torch.hub.load_state_dict_from_url(UpperCamelCase__,					map_location='cpu'      )

 __A	:		Optional[int]	  =      upgrade_state_dict(UpperCamelCase__,					UpperCamelCase__      )
 hf_model.load_state_dict(UpperCamelCase__      )
 __A	:		List[str]	  =      hf_model.state_dict()
 __A	:		Any	  =      count_parameters(UpperCamelCase__      )
 __A	:		Optional[Any]	  =      count_parameters(UpperCamelCase__      ) + count_parameters(UpperCamelCase__      )

 assert torch.allclose(UpperCamelCase__,					UpperCamelCase__,					atol=1E-3      )

 hf_model.save_pretrained(UpperCamelCase__      )


if __name__ == "__main__":
 UpperCAmelCase_			:  Tuple										=   argparse.ArgumentParser()
 parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
 parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to flava checkpoint')
 parser.add_argument('--codebook_path', default=None, type=str, help='Path to flava codebook checkpoint')
 parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
 UpperCAmelCase_			:  Optional[int]										=   parser.parse_args()

 convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)







"""simple docstring"""



import os



def 	_lowerCamelCase(	a = "matrix.txt"				):
       with open(os.path.join(os.path.dirname(a				)   ,    a				)				) as in_file:
              __a       =    in_file.read()

       __a       =    [[int(a				) for cell in row.split(","				)] for row in data.strip().splitlines()]
       __a       =    [[0 for cell in row] for row in grid]
       __a       =    len(grid[0]				)

       __a       =    [[0 for i in range(a				)] for j in range(a				)]
       __a       =    grid[0][0]
       for i in range(1   ,    a				):
              __a       =    grid[0][i] + dp[0][i - 1]
       for i in range(1   ,    a				):
              __a       =    grid[i][0] + dp[i - 1][0]

       for i in range(1   ,    a				):
              for j in range(1   ,    a				):
                     __a       =    grid[i][j] + min(dp[i - 1][j]   ,    dp[i][j - 1]				)

       return dp[-1][-1]


if __name__ == "__main__":
   print(F'''{solution() = }''')
